Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations14170
Missing cells13802
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.4 MiB
Average record size in memory1.7 KiB

Variable types

Numeric12
DateTime4
Categorical7
Text7
Boolean1
Unsupported1

Alerts

Check-in has constant value "16:00"Constant
Check-out has constant value "11:00"Constant
ADR is highly overall correlated with Adultos and 3 other fieldsHigh correlation
Adelanto ya pagado is highly overall correlated with Numero_ReservaHigh correlation
Adultos is highly overall correlated with ADR and 2 other fieldsHigh correlation
Apartamento is highly overall correlated with Precio_Medio_HistoricoHigh correlation
Apartamentos_Ocupados is highly overall correlated with Numero_Reserva and 2 other fieldsHigh correlation
ComisiĂ³n incluida is highly overall correlated with ADR and 3 other fieldsHigh correlation
GENIUS is highly overall correlated with Portal de reservaHigh correlation
Numero_Huespedes is highly overall correlated with ADR and 3 other fieldsHigh correlation
Numero_Reserva is highly overall correlated with Adelanto ya pagado and 4 other fieldsHigh correlation
NĂºmero de noches is highly overall correlated with ComisiĂ³n incluida and 1 other fieldsHigh correlation
Pagado is highly overall correlated with Portal de reserva and 1 other fieldsHigh correlation
Porcentaje_Ocupacion is highly overall correlated with Apartamentos_Ocupados and 2 other fieldsHigh correlation
Portal de reserva is highly overall correlated with Adultos and 4 other fieldsHigh correlation
PosiciĂ³n is highly overall correlated with Apartamentos_Ocupados and 3 other fieldsHigh correlation
Precio is highly overall correlated with ADR and 2 other fieldsHigh correlation
Precio_Medio_Historico is highly overall correlated with ApartamentoHigh correlation
Adelanto ya pagado is highly imbalanced (99.5%)Imbalance
Estado is highly imbalanced (69.8%)Imbalance
Notas has 818 (5.8%) missing valuesMissing
Detalles de precios has 1585 (11.2%) missing valuesMissing
Numero_Reserva has 3795 (26.8%) missing valuesMissing
Mensaje_Huesped has 3802 (26.8%) missing valuesMissing
BOOKING_NOTE has 3802 (26.8%) missing valuesMissing
Huésped_Token has unique valuesUnique
Teléfono_Token has unique valuesUnique
Email_Token has unique valuesUnique
Lead_Time is an unsupported type, check if it needs cleaning or further analysisUnsupported
Adultos has 974 (6.9%) zerosZeros
Niños has 11268 (79.5%) zerosZeros
ComisiĂ³n incluida has 1597 (11.3%) zerosZeros
Numero_Huespedes has 972 (6.9%) zerosZeros

Reproduction

Analysis started2024-08-28 06:52:35.044618
Analysis finished2024-08-28 06:55:00.560852
Duration2 minutes and 25.52 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

PosiciĂ³n
Real number (ℝ)

HIGH CORRELATION 

Distinct14153
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42539455
Minimum15390136
Maximum69854666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:00.626398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15390136
5-th percentile20312293
Q129896706
median42156263
Q353922308
95-th percentile66426171
Maximum69854666
Range54464530
Interquartile range (IQR)24025602

Descriptive statistics

Standard deviation14359047
Coefficient of variation (CV)0.33754657
Kurtosis-1.0508953
Mean42539455
Median Absolute Deviation (MAD)12034506
Skewness0.065784625
Sum6.0278408 Ă— 1011
Variance2.0618224 Ă— 1014
MonotonicityNot monotonic
2024-08-28T08:55:00.710506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50677661 2
 
< 0.1%
50757695 2
 
< 0.1%
50895260 2
 
< 0.1%
51202076 2
 
< 0.1%
50702258 2
 
< 0.1%
49378100 2
 
< 0.1%
50855651 2
 
< 0.1%
49003082 2
 
< 0.1%
50738435 2
 
< 0.1%
49082867 2
 
< 0.1%
Other values (14143) 14150
99.9%
ValueCountFrequency (%)
15390136 1
< 0.1%
16811706 1
< 0.1%
16912662 1
< 0.1%
16982857 1
< 0.1%
16993282 1
< 0.1%
16993297 1
< 0.1%
17038186 1
< 0.1%
17061199 1
< 0.1%
17061202 1
< 0.1%
17112598 1
< 0.1%
ValueCountFrequency (%)
69854666 1
< 0.1%
69849111 1
< 0.1%
69847911 1
< 0.1%
69843366 1
< 0.1%
69840491 1
< 0.1%
69840476 1
< 0.1%
69838566 1
< 0.1%
69834011 1
< 0.1%
69818721 1
< 0.1%
69810141 1
< 0.1%
Distinct1015
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size221.4 KiB
Minimum2022-01-01 00:00:00
Maximum2024-12-07 00:00:00
2024-08-28T08:55:00.805274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:55:00.892393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Salida
Date

Distinct1019
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size221.4 KiB
Minimum2022-01-02 00:00:00
Maximum2024-12-08 00:00:00
2024-08-28T08:55:00.975641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:55:01.061234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Apartamento
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
HD BRUNO
 
691
H BMA PRAGA
 
685
H - BUA 3P
 
643
H BMA BERLIN
 
642
H - BUA 4P
 
624
Other values (36)
10885 

Length

Max length21
Median length16
Mean length10.860409
Min length8

Characters and Unicode

Total characters153892
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowH BMA MONACO
2nd rowH BMA HELSINKI
3rd rowH BMA DUBLIN
4th rowHD-GARAJE 5
5th rowHD DARIO

Common Values

ValueCountFrequency (%)
HD BRUNO 691
 
4.9%
H BMA PRAGA 685
 
4.8%
H - BUA 3P 643
 
4.5%
H BMA BERLIN 642
 
4.5%
H - BUA 4P 624
 
4.4%
H BMA OSLO 592
 
4.2%
H BMA MONACO 560
 
4.0%
HD ELENA 560
 
4.0%
HD CELESTE 538
 
3.8%
H BMA AMSTERDAM 531
 
3.7%
Other values (31) 8104
57.2%

Length

2024-08-28T08:55:01.143558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
h 6493
17.8%
bma 5150
 
14.1%
hd 4865
 
13.3%
1343
 
3.7%
bua 1343
 
3.7%
hg0 1293
 
3.5%
hg1 907
 
2.5%
bruno 691
 
1.9%
praga 685
 
1.9%
3p 643
 
1.8%
Other values (41) 13098
35.9%

Most occurring characters

ValueCountFrequency (%)
22341
14.5%
A 20951
13.6%
H 15995
 
10.4%
B 9565
 
6.2%
D 8501
 
5.5%
E 7433
 
4.8%
R 7426
 
4.8%
M 7364
 
4.8%
I 6968
 
4.5%
O 6612
 
4.3%
Other values (23) 40736
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 153892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22341
14.5%
A 20951
13.6%
H 15995
 
10.4%
B 9565
 
6.2%
D 8501
 
5.5%
E 7433
 
4.8%
R 7426
 
4.8%
M 7364
 
4.8%
I 6968
 
4.5%
O 6612
 
4.3%
Other values (23) 40736
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 153892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22341
14.5%
A 20951
13.6%
H 15995
 
10.4%
B 9565
 
6.2%
D 8501
 
5.5%
E 7433
 
4.8%
R 7426
 
4.8%
M 7364
 
4.8%
I 6968
 
4.5%
O 6612
 
4.3%
Other values (23) 40736
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 153892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22341
14.5%
A 20951
13.6%
H 15995
 
10.4%
B 9565
 
6.2%
D 8501
 
5.5%
E 7433
 
4.8%
R 7426
 
4.8%
M 7364
 
4.8%
I 6968
 
4.5%
O 6612
 
4.3%
Other values (23) 40736
26.5%

Portal de reserva
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Booking.com
10387 
Airbnb
2211 
Reserva directa
1267 
PĂ¡gina web
 
305

Length

Max length15
Median length11
Mean length10.555963
Min length6

Characters and Unicode

Total characters149578
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBooking.com
2nd rowBooking.com
3rd rowBooking.com
4th rowReserva directa
5th rowBooking.com

Common Values

ValueCountFrequency (%)
Booking.com 10387
73.3%
Airbnb 2211
 
15.6%
Reserva directa 1267
 
8.9%
PĂ¡gina web 305
 
2.2%

Length

2024-08-28T08:55:01.211648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T08:55:01.295865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
booking.com 10387
66.0%
airbnb 2211
 
14.0%
reserva 1267
 
8.0%
directa 1267
 
8.0%
pĂ¡gina 305
 
1.9%
web 305
 
1.9%

Most occurring characters

ValueCountFrequency (%)
o 31161
20.8%
i 14170
9.5%
n 12903
8.6%
c 11654
 
7.8%
g 10692
 
7.1%
B 10387
 
6.9%
k 10387
 
6.9%
. 10387
 
6.9%
m 10387
 
6.9%
r 4745
 
3.2%
Other values (13) 22705
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 149578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 31161
20.8%
i 14170
9.5%
n 12903
8.6%
c 11654
 
7.8%
g 10692
 
7.1%
B 10387
 
6.9%
k 10387
 
6.9%
. 10387
 
6.9%
m 10387
 
6.9%
r 4745
 
3.2%
Other values (13) 22705
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 149578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 31161
20.8%
i 14170
9.5%
n 12903
8.6%
c 11654
 
7.8%
g 10692
 
7.1%
B 10387
 
6.9%
k 10387
 
6.9%
. 10387
 
6.9%
m 10387
 
6.9%
r 4745
 
3.2%
Other values (13) 22705
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 149578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 31161
20.8%
i 14170
9.5%
n 12903
8.6%
c 11654
 
7.8%
g 10692
 
7.1%
B 10387
 
6.9%
k 10387
 
6.9%
. 10387
 
6.9%
m 10387
 
6.9%
r 4745
 
3.2%
Other values (13) 22705
15.2%

Creado
Date

Distinct13652
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size221.4 KiB
Minimum2021-02-12 11:22:00
Maximum2024-12-08 23:16:00
2024-08-28T08:55:01.369948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:55:01.455015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Adultos
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4637262
Minimum0
Maximum8
Zeros974
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:01.522573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4825747
Coefficient of variation (CV)0.60176115
Kurtosis1.3205562
Mean2.4637262
Median Absolute Deviation (MAD)1
Skewness0.93997218
Sum34911
Variance2.1980278
MonotonicityNot monotonic
2024-08-28T08:55:01.590554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 6078
42.9%
3 2094
 
14.8%
1 1943
 
13.7%
4 1893
 
13.4%
0 974
 
6.9%
5 525
 
3.7%
6 387
 
2.7%
7 197
 
1.4%
8 79
 
0.6%
ValueCountFrequency (%)
0 974
 
6.9%
1 1943
 
13.7%
2 6078
42.9%
3 2094
 
14.8%
4 1893
 
13.4%
5 525
 
3.7%
6 387
 
2.7%
7 197
 
1.4%
8 79
 
0.6%
ValueCountFrequency (%)
8 79
 
0.6%
7 197
 
1.4%
6 387
 
2.7%
5 525
 
3.7%
4 1893
 
13.4%
3 2094
 
14.8%
2 6078
42.9%
1 1943
 
13.7%
0 974
 
6.9%

Niños
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36076217
Minimum0
Maximum7
Zeros11268
Zeros (%)79.5%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:01.657082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78204965
Coefficient of variation (CV)2.1677706
Kurtosis4.6467505
Mean0.36076217
Median Absolute Deviation (MAD)0
Skewness2.2074318
Sum5112
Variance0.61160165
MonotonicityNot monotonic
2024-08-28T08:55:01.717725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 11268
79.5%
2 1493
 
10.5%
1 1086
 
7.7%
3 269
 
1.9%
4 43
 
0.3%
5 6
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 11268
79.5%
1 1086
 
7.7%
2 1493
 
10.5%
3 269
 
1.9%
4 43
 
0.3%
5 6
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 4
 
< 0.1%
5 6
 
< 0.1%
4 43
 
0.3%
3 269
 
1.9%
2 1493
 
10.5%
1 1086
 
7.7%
0 11268
79.5%

Check-in
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size968.7 KiB
16:00
14170 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters70850
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16:00
2nd row16:00
3rd row16:00
4th row16:00
5th row16:00

Common Values

ValueCountFrequency (%)
16:00 14170
100.0%

Length

2024-08-28T08:55:01.788583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T08:55:01.844574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
16:00 14170
100.0%

Most occurring characters

ValueCountFrequency (%)
0 28340
40.0%
1 14170
20.0%
6 14170
20.0%
: 14170
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 28340
40.0%
1 14170
20.0%
6 14170
20.0%
: 14170
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 28340
40.0%
1 14170
20.0%
6 14170
20.0%
: 14170
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 28340
40.0%
1 14170
20.0%
6 14170
20.0%
: 14170
20.0%

Check-out
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size968.7 KiB
11:00
14170 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters70850
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11:00
2nd row11:00
3rd row11:00
4th row11:00
5th row11:00

Common Values

ValueCountFrequency (%)
11:00 14170
100.0%

Length

2024-08-28T08:55:01.905722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T08:55:01.963768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
11:00 14170
100.0%

Most occurring characters

ValueCountFrequency (%)
1 28340
40.0%
0 28340
40.0%
: 14170
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28340
40.0%
0 28340
40.0%
: 14170
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28340
40.0%
0 28340
40.0%
: 14170
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28340
40.0%
0 28340
40.0%
: 14170
20.0%

Notas
Text

MISSING 

Distinct12819
Distinct (%)96.0%
Missing818
Missing (%)5.8%
Memory size7.5 MiB
2024-08-28T08:55:02.142742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length1115
Median length921
Mean length246.7649
Min length3

Characters and Unicode

Total characters3294805
Distinct characters271
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12450 ?
Unique (%)93.2%

Sample

1st rowNĂºmero de reserva: 3509184945 Mensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 2.5839 MĂ¡s informaciĂ³n: booker_is_genius Pago por adelantado: 234.90EUR
2nd rowNĂºmero de reserva: 3677697588 Mensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** Approximate time of arrival: between 14:00 and 15:00 BOOKING NOTE : Payment charge is EUR 1.045 MĂ¡s informaciĂ³n: booker_is_genius Pago por adelantado: 95.00EUR
3rd rowNĂºmero de reserva: 2331981900 Mensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** Approximate time of arrival: between 17:00 and 18:00 BOOKING NOTE : Payment charge is EUR 2.695 MĂ¡s informaciĂ³n: booker_is_genius Pago por adelantado: 245.00EUR
4th rowTrjeta 28.12
5th rowNĂºmero de reserva: 3390950867 Mensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 2.211 MĂ¡s informaciĂ³n: booker_is_genius Pago por adelantado: 201.00EUR
ValueCountFrequency (%)
33554
 
7.4%
de 15203
 
3.3%
reserva 14291
 
3.1%
del 14163
 
3.1%
nĂºmero 12598
 
2.8%
huésped 12570
 
2.8%
por 11071
 
2.4%
mĂ¡s 10601
 
2.3%
has 10594
 
2.3%
is 10568
 
2.3%
Other values (26516) 309583
68.1%
2024-08-28T08:55:02.425757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
394201
 
12.0%
e 238075
 
7.2%
a 181190
 
5.5%
o 132327
 
4.0%
r 127194
 
3.9%
n 122200
 
3.7%
s 119957
 
3.6%
i 113817
 
3.5%
d 90983
 
2.8%
E 86543
 
2.6%
Other values (261) 1688318
51.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3294805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
394201
 
12.0%
e 238075
 
7.2%
a 181190
 
5.5%
o 132327
 
4.0%
r 127194
 
3.9%
n 122200
 
3.7%
s 119957
 
3.6%
i 113817
 
3.5%
d 90983
 
2.8%
E 86543
 
2.6%
Other values (261) 1688318
51.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3294805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
394201
 
12.0%
e 238075
 
7.2%
a 181190
 
5.5%
o 132327
 
4.0%
r 127194
 
3.9%
n 122200
 
3.7%
s 119957
 
3.6%
i 113817
 
3.5%
d 90983
 
2.8%
E 86543
 
2.6%
Other values (261) 1688318
51.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3294805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
394201
 
12.0%
e 238075
 
7.2%
a 181190
 
5.5%
o 132327
 
4.0%
r 127194
 
3.9%
n 122200
 
3.7%
s 119957
 
3.6%
i 113817
 
3.5%
d 90983
 
2.8%
E 86543
 
2.6%
Other values (261) 1688318
51.2%

Precio
Real number (ℝ)

HIGH CORRELATION 

Distinct2510
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.07198
Minimum1.15
Maximum7847.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:02.509864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.15
5-th percentile45
Q182
median148
Q3272
95-th percentile673.8875
Maximum7847.6
Range7846.45
Interquartile range (IQR)190

Descriptive statistics

Standard deviation291.79639
Coefficient of variation (CV)1.268283
Kurtosis130.74719
Mean230.07198
Median Absolute Deviation (MAD)77
Skewness7.8019184
Sum3260120
Variance85145.131
MonotonicityNot monotonic
2024-08-28T08:55:02.596548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 280
 
2.0%
20 258
 
1.8%
71 219
 
1.5%
77 206
 
1.5%
59 160
 
1.1%
83 155
 
1.1%
60 140
 
1.0%
53.1 135
 
1.0%
40 132
 
0.9%
95 127
 
0.9%
Other values (2500) 12358
87.2%
ValueCountFrequency (%)
1.15 1
 
< 0.1%
2.25 1
 
< 0.1%
3 1
 
< 0.1%
8.91 1
 
< 0.1%
10 26
0.2%
11 1
 
< 0.1%
13 1
 
< 0.1%
13.5 1
 
< 0.1%
15 25
0.2%
17.25 1
 
< 0.1%
ValueCountFrequency (%)
7847.6 2
< 0.1%
7330 1
< 0.1%
5785 1
< 0.1%
5595.25 1
< 0.1%
4097 1
< 0.1%
3886 1
< 0.1%
3800 1
< 0.1%
3738.2 1
< 0.1%
3630 1
< 0.1%
3600 1
< 0.1%

Detalles de precios
Text

MISSING 

Distinct3718
Distinct (%)29.5%
Missing1585
Missing (%)11.2%
Memory size1.2 MiB
2024-08-28T08:55:02.790784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length183
Median length123
Mean length26.489869
Min length11

Characters and Unicode

Total characters333375
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2074 ?
Unique (%)16.5%

Sample

1st rowTVA - EUR 21.35
2nd rowTVA - EUR 8.64
3rd rowTVA - EUR 22.27
4th rowIVA - EUR 18.27
5th rowTVA - EUR 20.27
ValueCountFrequency (%)
eur 16381
22.2%
16381
22.2%
iva 5292
 
7.2%
tva 5082
 
6.9%
cancellation 4424
 
6.0%
fee 2212
 
3.0%
host 2212
 
3.0%
payout 2212
 
3.0%
price 1579
 
2.1%
security 1579
 
2.1%
Other values (2951) 16393
22.2%
2024-08-28T08:55:03.075313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57366
 
17.2%
- 16381
 
4.9%
E 16381
 
4.9%
U 16381
 
4.9%
R 16381
 
4.9%
. 13796
 
4.1%
e 12006
 
3.6%
a 11068
 
3.3%
1 10716
 
3.2%
t 10435
 
3.1%
Other values (30) 152464
45.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
57366
 
17.2%
- 16381
 
4.9%
E 16381
 
4.9%
U 16381
 
4.9%
R 16381
 
4.9%
. 13796
 
4.1%
e 12006
 
3.6%
a 11068
 
3.3%
1 10716
 
3.2%
t 10435
 
3.1%
Other values (30) 152464
45.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
57366
 
17.2%
- 16381
 
4.9%
E 16381
 
4.9%
U 16381
 
4.9%
R 16381
 
4.9%
. 13796
 
4.1%
e 12006
 
3.6%
a 11068
 
3.3%
1 10716
 
3.2%
t 10435
 
3.1%
Other values (30) 152464
45.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
57366
 
17.2%
- 16381
 
4.9%
E 16381
 
4.9%
U 16381
 
4.9%
R 16381
 
4.9%
. 13796
 
4.1%
e 12006
 
3.6%
a 11068
 
3.3%
1 10716
 
3.2%
t 10435
 
3.1%
Other values (30) 152464
45.7%

ComisiĂ³n incluida
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2221
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.099969
Minimum0
Maximum867.75
Zeros1597
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:03.165507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.65
median20.535
Q338.34
95-th percentile95.1995
Maximum867.75
Range867.75
Interquartile range (IQR)27.69

Descriptive statistics

Standard deviation37.941852
Coefficient of variation (CV)1.2199965
Kurtosis59.489679
Mean31.099969
Median Absolute Deviation (MAD)11.355
Skewness5.1843902
Sum440686.56
Variance1439.5841
MonotonicityNot monotonic
2024-08-28T08:55:03.247018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1597
 
11.3%
9.75 264
 
1.9%
10.65 223
 
1.6%
11.55 197
 
1.4%
12.45 162
 
1.1%
8.85 157
 
1.1%
7.96 135
 
1.0%
21.3 125
 
0.9%
14.25 124
 
0.9%
8.78 122
 
0.9%
Other values (2211) 11064
78.1%
ValueCountFrequency (%)
0 1597
11.3%
0.17 1
 
< 0.1%
0.34 1
 
< 0.1%
0.45 1
 
< 0.1%
1.34 1
 
< 0.1%
1.5 1
 
< 0.1%
1.65 1
 
< 0.1%
1.95 1
 
< 0.1%
2.02 1
 
< 0.1%
2.25 1
 
< 0.1%
ValueCountFrequency (%)
867.75 1
< 0.1%
839.29 1
< 0.1%
614.55 1
< 0.1%
582.9 1
< 0.1%
560.73 1
< 0.1%
502.95 1
< 0.1%
492 1
< 0.1%
457.5 2
< 0.1%
445.45 1
< 0.1%
443.27 1
< 0.1%

Pagado
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
SĂ­
11449 
No
2721 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters28340
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSĂ­
2nd rowSĂ­
3rd rowSĂ­
4th rowNo
5th rowSĂ­

Common Values

ValueCountFrequency (%)
SĂ­ 11449
80.8%
No 2721
 
19.2%

Length

2024-08-28T08:55:03.325617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T08:55:03.382861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sĂ­ 11449
80.8%
no 2721
 
19.2%

Most occurring characters

ValueCountFrequency (%)
S 11449
40.4%
Ă­ 11449
40.4%
N 2721
 
9.6%
o 2721
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 11449
40.4%
Ă­ 11449
40.4%
N 2721
 
9.6%
o 2721
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 11449
40.4%
Ă­ 11449
40.4%
N 2721
 
9.6%
o 2721
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 11449
40.4%
Ă­ 11449
40.4%
N 2721
 
9.6%
o 2721
 
9.6%

Adelanto ya pagado
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size927.3 KiB
No
14165 
SĂ­
 
5

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters28340
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 14165
> 99.9%
SĂ­ 5
 
< 0.1%

Length

2024-08-28T08:55:03.448393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T08:55:03.507948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 14165
> 99.9%
sĂ­ 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 14165
50.0%
o 14165
50.0%
S 5
 
< 0.1%
Ă­ 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 14165
50.0%
o 14165
50.0%
S 5
 
< 0.1%
Ă­ 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 14165
50.0%
o 14165
50.0%
S 5
 
< 0.1%
Ă­ 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 14165
50.0%
o 14165
50.0%
S 5
 
< 0.1%
Ă­ 5
 
< 0.1%

NĂºmero de noches
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8161609
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:03.570460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum81
Range80
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9504429
Coefficient of variation (CV)1.0739373
Kurtosis382.5878
Mean1.8161609
Median Absolute Deviation (MAD)0
Skewness14.20734
Sum25735
Variance3.8042275
MonotonicityNot monotonic
2024-08-28T08:55:03.650916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 8206
57.9%
2 3436
24.2%
3 1407
 
9.9%
4 609
 
4.3%
5 258
 
1.8%
7 78
 
0.6%
6 69
 
0.5%
8 22
 
0.2%
11 11
 
0.1%
9 10
 
0.1%
Other values (19) 64
 
0.5%
ValueCountFrequency (%)
1 8206
57.9%
2 3436
24.2%
3 1407
 
9.9%
4 609
 
4.3%
5 258
 
1.8%
6 69
 
0.5%
7 78
 
0.6%
8 22
 
0.2%
9 10
 
0.1%
10 10
 
0.1%
ValueCountFrequency (%)
81 1
 
< 0.1%
62 2
 
< 0.1%
33 1
 
< 0.1%
31 6
< 0.1%
30 5
< 0.1%
29 2
 
< 0.1%
28 1
 
< 0.1%
27 4
< 0.1%
24 1
 
< 0.1%
21 2
 
< 0.1%

Estado
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Reservado
12745 
Cancelado
1410 
Overbooking
 
15

Length

Max length11
Median length9
Mean length9.0021171
Min length9

Characters and Unicode

Total characters127560
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReservado
2nd rowReservado
3rd rowReservado
4th rowReservado
5th rowReservado

Common Values

ValueCountFrequency (%)
Reservado 12745
89.9%
Cancelado 1410
 
10.0%
Overbooking 15
 
0.1%

Length

2024-08-28T08:55:03.736614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-28T08:55:03.808753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
reservado 12745
89.9%
cancelado 1410
 
10.0%
overbooking 15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 26915
21.1%
a 15565
12.2%
o 14185
11.1%
d 14155
11.1%
r 12760
10.0%
v 12760
10.0%
R 12745
10.0%
s 12745
10.0%
n 1425
 
1.1%
l 1410
 
1.1%
Other values (7) 2895
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 26915
21.1%
a 15565
12.2%
o 14185
11.1%
d 14155
11.1%
r 12760
10.0%
v 12760
10.0%
R 12745
10.0%
s 12745
10.0%
n 1425
 
1.1%
l 1410
 
1.1%
Other values (7) 2895
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 26915
21.1%
a 15565
12.2%
o 14185
11.1%
d 14155
11.1%
r 12760
10.0%
v 12760
10.0%
R 12745
10.0%
s 12745
10.0%
n 1425
 
1.1%
l 1410
 
1.1%
Other values (7) 2895
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 26915
21.1%
a 15565
12.2%
o 14185
11.1%
d 14155
11.1%
r 12760
10.0%
v 12760
10.0%
R 12745
10.0%
s 12745
10.0%
n 1425
 
1.1%
l 1410
 
1.1%
Other values (7) 2895
 
2.3%

Huésped_Token
Text

UNIQUE 

Distinct14170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-08-28T08:55:03.926651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters510120
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14170 ?
Unique (%)100.0%

Sample

1st rowbe87b7b4-734b-475a-aedc-b80420360db2
2nd row35e44167-2433-4876-b49b-a4545f1f7838
3rd row36ce4d92-6974-4f4d-be07-64ca05a4d878
4th row5ea7bfa0-4a1a-471a-bbfd-c8ab3d45306b
5th row9a4df539-fa60-48b4-ba02-c971b89cd539
ValueCountFrequency (%)
be87b7b4-734b-475a-aedc-b80420360db2 1
 
< 0.1%
76633f0b-aafc-420e-bffe-7454323e896c 1
 
< 0.1%
5aeb6f79-8019-4ea1-ad9b-bf9cf2edff5f 1
 
< 0.1%
72f1fee8-9e5e-46a5-ad11-0e1270205abb 1
 
< 0.1%
36ce4d92-6974-4f4d-be07-64ca05a4d878 1
 
< 0.1%
5ea7bfa0-4a1a-471a-bbfd-c8ab3d45306b 1
 
< 0.1%
9a4df539-fa60-48b4-ba02-c971b89cd539 1
 
< 0.1%
b4a1bb5d-b1ff-430d-aed2-0d7c14fe9287 1
 
< 0.1%
24977c99-4a49-49f7-8b79-b242634b67a8 1
 
< 0.1%
89bbed20-7c13-4d1a-bc20-14040d849e1f 1
 
< 0.1%
Other values (14160) 14160
99.9%
2024-08-28T08:55:04.129159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 56680
 
11.1%
4 41080
 
8.1%
a 30286
 
5.9%
9 30115
 
5.9%
b 30005
 
5.9%
8 29911
 
5.9%
2 26918
 
5.3%
c 26861
 
5.3%
1 26703
 
5.2%
f 26585
 
5.2%
Other values (7) 184976
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 510120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 56680
 
11.1%
4 41080
 
8.1%
a 30286
 
5.9%
9 30115
 
5.9%
b 30005
 
5.9%
8 29911
 
5.9%
2 26918
 
5.3%
c 26861
 
5.3%
1 26703
 
5.2%
f 26585
 
5.2%
Other values (7) 184976
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 510120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 56680
 
11.1%
4 41080
 
8.1%
a 30286
 
5.9%
9 30115
 
5.9%
b 30005
 
5.9%
8 29911
 
5.9%
2 26918
 
5.3%
c 26861
 
5.3%
1 26703
 
5.2%
f 26585
 
5.2%
Other values (7) 184976
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 510120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 56680
 
11.1%
4 41080
 
8.1%
a 30286
 
5.9%
9 30115
 
5.9%
b 30005
 
5.9%
8 29911
 
5.9%
2 26918
 
5.3%
c 26861
 
5.3%
1 26703
 
5.2%
f 26585
 
5.2%
Other values (7) 184976
36.3%

Teléfono_Token
Text

UNIQUE 

Distinct14170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-08-28T08:55:04.262099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters510120
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14170 ?
Unique (%)100.0%

Sample

1st rowe0c4f0b2-b56a-47ba-acb8-f6a35e572a76
2nd row415c6f96-47af-404d-aa92-720fc0218919
3rd row0a952103-56e5-4232-876f-eba3826afb24
4th rowadcea205-d9d3-45f5-84df-c636276779c2
5th rowb240d5b6-2622-4361-a2d4-4815bb25f647
ValueCountFrequency (%)
e0c4f0b2-b56a-47ba-acb8-f6a35e572a76 1
 
< 0.1%
8b7f1d8a-971f-4800-a519-7e31ed715fa3 1
 
< 0.1%
c280c472-780e-4e50-8bab-0c5905d38c01 1
 
< 0.1%
06006122-94f2-45b8-ab0c-22810624f274 1
 
< 0.1%
0a952103-56e5-4232-876f-eba3826afb24 1
 
< 0.1%
adcea205-d9d3-45f5-84df-c636276779c2 1
 
< 0.1%
b240d5b6-2622-4361-a2d4-4815bb25f647 1
 
< 0.1%
2a189e60-4a59-4efe-9ee6-ba392ff23842 1
 
< 0.1%
74366778-7b2f-40a3-a7c4-b88271cc0424 1
 
< 0.1%
3b520c09-8acf-4018-aa96-fea5e1b20f77 1
 
< 0.1%
Other values (14160) 14160
99.9%
2024-08-28T08:55:04.464107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 56680
 
11.1%
4 40856
 
8.0%
a 30383
 
6.0%
b 30148
 
5.9%
9 30131
 
5.9%
8 30107
 
5.9%
0 26849
 
5.3%
5 26728
 
5.2%
6 26650
 
5.2%
e 26562
 
5.2%
Other values (7) 185026
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 510120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 56680
 
11.1%
4 40856
 
8.0%
a 30383
 
6.0%
b 30148
 
5.9%
9 30131
 
5.9%
8 30107
 
5.9%
0 26849
 
5.3%
5 26728
 
5.2%
6 26650
 
5.2%
e 26562
 
5.2%
Other values (7) 185026
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 510120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 56680
 
11.1%
4 40856
 
8.0%
a 30383
 
6.0%
b 30148
 
5.9%
9 30131
 
5.9%
8 30107
 
5.9%
0 26849
 
5.3%
5 26728
 
5.2%
6 26650
 
5.2%
e 26562
 
5.2%
Other values (7) 185026
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 510120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 56680
 
11.1%
4 40856
 
8.0%
a 30383
 
6.0%
b 30148
 
5.9%
9 30131
 
5.9%
8 30107
 
5.9%
0 26849
 
5.3%
5 26728
 
5.2%
6 26650
 
5.2%
e 26562
 
5.2%
Other values (7) 185026
36.3%

Email_Token
Text

UNIQUE 

Distinct14170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-08-28T08:55:04.598310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters510120
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14170 ?
Unique (%)100.0%

Sample

1st row8f5e9611-e2d7-4299-b5cd-8ffa3780cc21
2nd row513be7e2-5acb-40f1-994d-8b5b71755b3d
3rd row81c733f5-6f95-42db-a4ff-6d569ff29ee0
4th rowdeb8b9cf-d436-405c-884e-890910c45722
5th row2b196f6c-9987-441b-a20a-0ab7bdbcc1f2
ValueCountFrequency (%)
8f5e9611-e2d7-4299-b5cd-8ffa3780cc21 1
 
< 0.1%
adce2f02-3c68-44aa-8382-73258afea378 1
 
< 0.1%
791d9a60-6175-48d0-a960-b9a71329b1d9 1
 
< 0.1%
1df2f3a1-9055-4620-aef7-8827078fa422 1
 
< 0.1%
81c733f5-6f95-42db-a4ff-6d569ff29ee0 1
 
< 0.1%
deb8b9cf-d436-405c-884e-890910c45722 1
 
< 0.1%
2b196f6c-9987-441b-a20a-0ab7bdbcc1f2 1
 
< 0.1%
f10d0bc9-6c2d-4ef8-afc9-ff8a74f2418c 1
 
< 0.1%
2d83ef75-5f19-4afc-b60f-b7e71200b069 1
 
< 0.1%
c8bd578f-c9cd-428a-b1c7-07c1c611b11d 1
 
< 0.1%
Other values (14160) 14160
99.9%
2024-08-28T08:55:04.799624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 56680
 
11.1%
4 40799
 
8.0%
b 30274
 
5.9%
a 30205
 
5.9%
9 30203
 
5.9%
8 29963
 
5.9%
c 26969
 
5.3%
2 26792
 
5.3%
e 26657
 
5.2%
5 26640
 
5.2%
Other values (7) 184938
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 510120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 56680
 
11.1%
4 40799
 
8.0%
b 30274
 
5.9%
a 30205
 
5.9%
9 30203
 
5.9%
8 29963
 
5.9%
c 26969
 
5.3%
2 26792
 
5.3%
e 26657
 
5.2%
5 26640
 
5.2%
Other values (7) 184938
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 510120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 56680
 
11.1%
4 40799
 
8.0%
b 30274
 
5.9%
a 30205
 
5.9%
9 30203
 
5.9%
8 29963
 
5.9%
c 26969
 
5.3%
2 26792
 
5.3%
e 26657
 
5.2%
5 26640
 
5.2%
Other values (7) 184938
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 510120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 56680
 
11.1%
4 40799
 
8.0%
b 30274
 
5.9%
a 30205
 
5.9%
9 30203
 
5.9%
8 29963
 
5.9%
c 26969
 
5.3%
2 26792
 
5.3%
e 26657
 
5.2%
5 26640
 
5.2%
Other values (7) 184938
36.3%

Numero_Reserva
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10000
Distinct (%)96.4%
Missing3795
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean3.6433743 Ă— 109
Minimum1.0059188 Ă— 109
Maximum5.0001592 Ă— 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:04.891208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.0059188 Ă— 109
5-th percentile2.2560339 Ă— 109
Q12.9356457 Ă— 109
median3.8166494 Ă— 109
Q34.2660505 Ă— 109
95-th percentile4.8337858 Ă— 109
Maximum5.0001592 Ă— 109
Range3.9942404 Ă— 109
Interquartile range (IQR)1.3304048 Ă— 109

Descriptive statistics

Standard deviation8.2879556 Ă— 108
Coefficient of variation (CV)0.22748021
Kurtosis-0.8676339
Mean3.6433743 Ă— 109
Median Absolute Deviation (MAD)6.1324383 Ă— 108
Skewness-0.35952869
Sum3.7800008 Ă— 1013
Variance6.8690207 Ă— 1017
MonotonicityNot monotonic
2024-08-28T08:55:04.981765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4283082648 8
 
0.1%
4208534781 6
 
< 0.1%
4242031213 5
 
< 0.1%
4717565131 5
 
< 0.1%
3234695754 5
 
< 0.1%
2944693149 4
 
< 0.1%
3779868996 4
 
< 0.1%
4501914314 4
 
< 0.1%
3017001528 4
 
< 0.1%
4141710613 4
 
< 0.1%
Other values (9990) 10326
72.9%
(Missing) 3795
 
26.8%
ValueCountFrequency (%)
1005918761 1
< 0.1%
1005940672 1
< 0.1%
1007108187 1
< 0.1%
1015466631 1
< 0.1%
1021315351 1
< 0.1%
1021317261 1
< 0.1%
1021389587 1
< 0.1%
1029968432 1
< 0.1%
1062900362 1
< 0.1%
1062942205 1
< 0.1%
ValueCountFrequency (%)
5000159195 1
< 0.1%
4999865749 1
< 0.1%
4999810028 1
< 0.1%
4999203753 1
< 0.1%
4999049478 1
< 0.1%
4999029286 1
< 0.1%
4998728654 1
< 0.1%
4998674805 1
< 0.1%
4998670026 1
< 0.1%
4998637580 1
< 0.1%

Mensaje_Huesped
Text

MISSING 

Distinct1181
Distinct (%)11.4%
Missing3802
Missing (%)26.8%
Memory size1.4 MiB
2024-08-28T08:55:05.162481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length824
Median length0
Mean length32.658854
Min length0

Characters and Unicode

Total characters338607
Distinct characters108
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1104 ?
Unique (%)10.6%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
and 1214
 
2.9%
of 1212
 
2.9%
time 1189
 
2.9%
arrival 1184
 
2.9%
between 1183
 
2.9%
approximate 1179
 
2.9%
en 897
 
2.2%
smart 856
 
2.1%
flex 856
 
2.1%
de 778
 
1.9%
Other values (2784) 30609
74.4%
2024-08-28T08:55:05.423064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38323
 
11.3%
e 27439
 
8.1%
a 25668
 
7.6%
t 19319
 
5.7%
i 19249
 
5.7%
n 18730
 
5.5%
o 17789
 
5.3%
r 14588
 
4.3%
s 13743
 
4.1%
l 11412
 
3.4%
Other values (98) 132347
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 338607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
38323
 
11.3%
e 27439
 
8.1%
a 25668
 
7.6%
t 19319
 
5.7%
i 19249
 
5.7%
n 18730
 
5.5%
o 17789
 
5.3%
r 14588
 
4.3%
s 13743
 
4.1%
l 11412
 
3.4%
Other values (98) 132347
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 338607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
38323
 
11.3%
e 27439
 
8.1%
a 25668
 
7.6%
t 19319
 
5.7%
i 19249
 
5.7%
n 18730
 
5.5%
o 17789
 
5.3%
r 14588
 
4.3%
s 13743
 
4.1%
l 11412
 
3.4%
Other values (98) 132347
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 338607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
38323
 
11.3%
e 27439
 
8.1%
a 25668
 
7.6%
t 19319
 
5.7%
i 19249
 
5.7%
n 18730
 
5.5%
o 17789
 
5.3%
r 14588
 
4.3%
s 13743
 
4.1%
l 11412
 
3.4%
Other values (98) 132347
39.1%

BOOKING_NOTE
Text

MISSING 

Distinct2245
Distinct (%)21.7%
Missing3802
Missing (%)26.8%
Memory size1.1 MiB
2024-08-28T08:55:05.611356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length27.456308
Min length25

Characters and Unicode

Total characters284667
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1046 ?
Unique (%)10.1%

Sample

1st rowPayment charge is EUR 2.5839
2nd rowPayment charge is EUR 1.045
3rd rowPayment charge is EUR 2.695
4th rowPayment charge is EUR 2.211
5th rowPayment charge is EUR 2.453
ValueCountFrequency (%)
payment 10368
20.0%
eur 10368
20.0%
charge 10368
20.0%
is 10368
20.0%
0.715 175
 
0.3%
0.781 160
 
0.3%
0.649 154
 
0.3%
0.5841 136
 
0.3%
0.6435 121
 
0.2%
0.847 118
 
0.2%
Other values (2239) 9504
18.3%
2024-08-28T08:55:05.866081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41472
 
14.6%
e 20736
 
7.3%
a 20736
 
7.3%
P 10368
 
3.6%
g 10368
 
3.6%
. 10368
 
3.6%
R 10368
 
3.6%
U 10368
 
3.6%
s 10368
 
3.6%
i 10368
 
3.6%
Other values (18) 129147
45.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 284667
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
41472
 
14.6%
e 20736
 
7.3%
a 20736
 
7.3%
P 10368
 
3.6%
g 10368
 
3.6%
. 10368
 
3.6%
R 10368
 
3.6%
U 10368
 
3.6%
s 10368
 
3.6%
i 10368
 
3.6%
Other values (18) 129147
45.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 284667
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
41472
 
14.6%
e 20736
 
7.3%
a 20736
 
7.3%
P 10368
 
3.6%
g 10368
 
3.6%
. 10368
 
3.6%
R 10368
 
3.6%
U 10368
 
3.6%
s 10368
 
3.6%
i 10368
 
3.6%
Other values (18) 129147
45.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 284667
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
41472
 
14.6%
e 20736
 
7.3%
a 20736
 
7.3%
P 10368
 
3.6%
g 10368
 
3.6%
. 10368
 
3.6%
R 10368
 
3.6%
U 10368
 
3.6%
s 10368
 
3.6%
i 10368
 
3.6%
Other values (18) 129147
45.4%

GENIUS
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size124.5 KiB
True
9045 
False
5125 
ValueCountFrequency (%)
True 9045
63.8%
False 5125
36.2%
2024-08-28T08:55:05.939182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Numero_Huespedes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8244884
Minimum0
Maximum8
Zeros972
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:05.992536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6395601
Coefficient of variation (CV)0.5804804
Kurtosis0.27807467
Mean2.8244884
Median Absolute Deviation (MAD)1
Skewness0.52656568
Sum40023
Variance2.6881574
MonotonicityNot monotonic
2024-08-28T08:55:06.057212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 4108
29.0%
4 3226
22.8%
3 2413
17.0%
1 1763
12.4%
0 972
 
6.9%
5 776
 
5.5%
6 481
 
3.4%
7 313
 
2.2%
8 118
 
0.8%
ValueCountFrequency (%)
0 972
 
6.9%
1 1763
12.4%
2 4108
29.0%
3 2413
17.0%
4 3226
22.8%
5 776
 
5.5%
6 481
 
3.4%
7 313
 
2.2%
8 118
 
0.8%
ValueCountFrequency (%)
8 118
 
0.8%
7 313
 
2.2%
6 481
 
3.4%
5 776
 
5.5%
4 3226
22.8%
3 2413
17.0%
2 4108
29.0%
1 1763
12.4%
0 972
 
6.9%

Lead_Time
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size221.4 KiB

ADR
Real number (ℝ)

HIGH CORRELATION 

Distinct2433
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.8623
Minimum1.15
Maximum1502.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:06.132562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.15
5-th percentile37.8
Q171
median102
Q3158.33333
95-th percentile266.8875
Maximum1502.7
Range1501.55
Interquartile range (IQR)87.333333

Descriptive statistics

Standard deviation82.56586
Coefficient of variation (CV)0.66659396
Kurtosis19.410191
Mean123.8623
Median Absolute Deviation (MAD)38
Skewness2.8264713
Sum1755128.8
Variance6817.1212
MonotonicityNot monotonic
2024-08-28T08:55:06.213371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 425
 
3.0%
65 351
 
2.5%
71 326
 
2.3%
77 257
 
1.8%
83 223
 
1.6%
95 184
 
1.3%
59 178
 
1.3%
58.5 166
 
1.2%
53.1 160
 
1.1%
89 146
 
1.0%
Other values (2423) 11754
82.9%
ValueCountFrequency (%)
1.15 1
 
< 0.1%
2.25 1
 
< 0.1%
3 1
 
< 0.1%
3.333333333 1
 
< 0.1%
6.666666667 1
 
< 0.1%
7.5 1
 
< 0.1%
8.91 1
 
< 0.1%
10 74
0.5%
10.34482759 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
1502.7 1
< 0.1%
1145.6 1
< 0.1%
1072 1
< 0.1%
1043.825 1
< 0.1%
1013 1
< 0.1%
989.9 1
< 0.1%
974 1
< 0.1%
971.5 1
< 0.1%
964 1
< 0.1%
900 1
< 0.1%

Precio_Medio_Historico
Real number (ℝ)

HIGH CORRELATION 

Distinct13190
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.35999
Minimum10
Maximum331.66667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:06.295610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile38.543098
Q196.5833
median110.49509
Q3123.43788
95-th percentile188.73521
Maximum331.66667
Range321.66667
Interquartile range (IQR)26.854577

Descriptive statistics

Standard deviation41.017779
Coefficient of variation (CV)0.35867245
Kurtosis1.5013639
Mean114.35999
Median Absolute Deviation (MAD)13.546256
Skewness0.36875365
Sum1620481.1
Variance1682.4582
MonotonicityNot monotonic
2024-08-28T08:55:06.378671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 10
 
0.1%
97.40314123 4
 
< 0.1%
47 4
 
< 0.1%
20 4
 
< 0.1%
105.8033889 4
 
< 0.1%
184.7025629 4
 
< 0.1%
127.8431764 4
 
< 0.1%
103.1859795 4
 
< 0.1%
113.6441737 4
 
< 0.1%
99.10422892 4
 
< 0.1%
Other values (13180) 14124
99.7%
ValueCountFrequency (%)
10 1
< 0.1%
10.71428571 1
< 0.1%
10.83333333 1
< 0.1%
11 1
< 0.1%
11.25 1
< 0.1%
11.35 1
< 0.1%
11.37755102 1
< 0.1%
11.40625 1
< 0.1%
11.43617021 1
< 0.1%
11.46226415 1
< 0.1%
ValueCountFrequency (%)
331.6666667 1
< 0.1%
330 2
< 0.1%
309.5 1
< 0.1%
275.2 1
< 0.1%
256.5 1
< 0.1%
248.7142857 1
< 0.1%
241.218314 2
< 0.1%
241.1588109 2
< 0.1%
241.0827373 1
< 0.1%
240.9525992 1
< 0.1%

Fecha
Date

Distinct1015
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size221.4 KiB
Minimum2022-01-01 00:00:00
Maximum2024-12-07 00:00:00
2024-08-28T08:55:06.460154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:55:06.544442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Apartamentos_Ocupados
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.033945
Minimum4
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:06.620110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile23
Q123
median31
Q340
95-th percentile41
Maximum41
Range37
Interquartile range (IQR)17

Descriptive statistics

Standard deviation7.0173689
Coefficient of variation (CV)0.22611914
Kurtosis-0.8753354
Mean31.033945
Median Absolute Deviation (MAD)8
Skewness-0.042402423
Sum439751
Variance49.243466
MonotonicityNot monotonic
2024-08-28T08:55:06.693087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
31 3883
27.4%
23 3791
26.8%
40 2748
19.4%
30 1346
 
9.5%
41 1062
 
7.5%
39 390
 
2.8%
38 107
 
0.8%
22 86
 
0.6%
26 80
 
0.6%
28 78
 
0.6%
Other values (23) 599
 
4.2%
ValueCountFrequency (%)
4 2
 
< 0.1%
6 3
 
< 0.1%
7 6
 
< 0.1%
8 5
 
< 0.1%
9 8
 
0.1%
10 18
0.1%
11 17
0.1%
12 33
0.2%
13 21
0.1%
14 14
0.1%
ValueCountFrequency (%)
41 1062
 
7.5%
40 2748
19.4%
39 390
 
2.8%
38 107
 
0.8%
36 20
 
0.1%
34 24
 
0.2%
32 17
 
0.1%
31 3883
27.4%
30 1346
 
9.5%
29 42
 
0.3%

Porcentaje_Ocupacion
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.692549
Minimum9.7560976
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2024-08-28T08:55:06.766699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9.7560976
5-th percentile56.097561
Q156.097561
median75.609756
Q397.560976
95-th percentile100
Maximum100
Range90.243902
Interquartile range (IQR)41.463415

Descriptive statistics

Standard deviation17.115534
Coefficient of variation (CV)0.22611914
Kurtosis-0.8753354
Mean75.692549
Median Absolute Deviation (MAD)19.512195
Skewness-0.042402423
Sum1072563.4
Variance292.9415
MonotonicityNot monotonic
2024-08-28T08:55:06.840355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
75.6097561 3883
27.4%
56.09756098 3791
26.8%
97.56097561 2748
19.4%
73.17073171 1346
 
9.5%
100 1062
 
7.5%
95.12195122 390
 
2.8%
92.68292683 107
 
0.8%
53.65853659 86
 
0.6%
63.41463415 80
 
0.6%
68.29268293 78
 
0.6%
Other values (23) 599
 
4.2%
ValueCountFrequency (%)
9.756097561 2
 
< 0.1%
14.63414634 3
 
< 0.1%
17.07317073 6
 
< 0.1%
19.51219512 5
 
< 0.1%
21.95121951 8
 
0.1%
24.3902439 18
0.1%
26.82926829 17
0.1%
29.26829268 33
0.2%
31.70731707 21
0.1%
34.14634146 14
0.1%
ValueCountFrequency (%)
100 1062
 
7.5%
97.56097561 2748
19.4%
95.12195122 390
 
2.8%
92.68292683 107
 
0.8%
87.80487805 20
 
0.1%
82.92682927 24
 
0.2%
78.04878049 17
 
0.1%
75.6097561 3883
27.4%
73.17073171 1346
 
9.5%
70.73170732 42
 
0.3%

Interactions

2024-08-28T08:54:53.623814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:37.404767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:44.832849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:51.930234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:58.593146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:05.442881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:12.316627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:19.339891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:26.461020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:32.768624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:41.052632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.337761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:53.684284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:37.484797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:44.953259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:51.995126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:58.659688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:05.506087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:12.384718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:24.718746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:26.537987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:32.833780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:41.122076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.403012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:53.749806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:37.562055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:45.030830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:52.065520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:58.729651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:05.575058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:12.452215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:29.405411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:26.613241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:32.903149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:41.198432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.475083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:53.816494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:37.634801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:45.103989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:52.134086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:58.796333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:05.641394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:12.520185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:34.998065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:26.683413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:32.975400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:41.269377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.546418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:53.881165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:37.717534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:45.176971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:52.204740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:58.865223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:05.707889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:12.589690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:39.601437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:26.753944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:33.047387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:41.337133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.630769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:53.950406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:37.802678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:45.244711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:52.267219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:58.928513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:05.769007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:12.649813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:45.226753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:26.815170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:33.107849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:41.397336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.695262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:54.031860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:37.893520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:45.328758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:52.345238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:59.011035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:06.310434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:12.733455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:49.853742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:26.897516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:33.188494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:41.478092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.775164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:59.618136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:44.469933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:51.589882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:58.267680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:05.082330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:11.983635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:18.994794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:01.248725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:32.428824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:40.711102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.009978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:53.279375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:59.692626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:44.550192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:51.660619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:58.334597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:05.164399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:12.049802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:19.063905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:06.030697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:32.502766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:40.787740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.078042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:53.351110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:59.766743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:44.621899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:51.730268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:58.399249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:05.241843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:12.134427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:19.130666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:10.877203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:32.569156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:40.857584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.143930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:53.417422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:59.841543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:44.691952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:51.797576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:58.460138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:05.309371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:12.197072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:19.201410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:17.269715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:32.635189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:40.922750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.207981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:53.488270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:59.914481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:44.767375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:51.866195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:52:58.530290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:05.381459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:12.261897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:53:19.269096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:21.896939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:32.704948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:40.992313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:47.275056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-28T08:54:53.557723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-08-28T08:55:06.903541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ADRAdelanto ya pagadoAdultosApartamentoApartamentos_OcupadosComisiĂ³n incluidaEstadoGENIUSNiñosNumero_HuespedesNumero_ReservaNĂºmero de nochesPagadoPorcentaje_OcupacionPortal de reservaPosiciĂ³nPrecioPrecio_Medio_Historico
ADR1.0000.0000.6120.1670.1180.7240.0930.1190.2770.6940.0260.1160.1250.1180.0860.1330.7780.496
Adelanto ya pagado0.0001.0000.0500.0640.0000.0000.0000.0190.0000.0501.0000.0000.0330.0000.0580.0340.0000.044
Adultos0.6120.0501.0000.4020.0470.4460.0570.365-0.0230.853-0.020-0.0520.3830.0470.5050.0430.4150.434
Apartamento0.1670.0640.4021.0000.1190.0710.0590.4020.1150.4450.0000.0590.2980.1190.4750.1940.0820.683
Apartamentos_Ocupados0.1180.0000.0470.1191.0000.0570.0000.1520.0190.0490.703-0.0300.2771.0000.0630.8980.0640.265
ComisiĂ³n incluida0.7240.0000.4460.0710.0571.0000.0620.0710.2240.507-0.0090.5430.0770.0570.0510.0610.8710.318
Estado0.0930.0000.0570.0590.0000.0621.0000.0460.0000.0550.1200.0250.0810.0000.0870.0270.0620.040
GENIUS0.1190.0190.3650.4020.1520.0710.0461.0000.1640.3650.1890.0530.4180.1520.8020.1840.0300.295
Niños0.2770.000-0.0230.1150.0190.2240.0000.1641.0000.458-0.0090.0050.1210.0190.1110.0320.1950.169
Numero_Huespedes0.6940.0500.8530.4450.0490.5070.0550.3650.4581.000-0.025-0.0400.3840.0490.5040.0510.4740.482
Numero_Reserva0.0261.000-0.0200.0000.703-0.0090.1200.189-0.009-0.0251.000-0.0420.1880.7031.0000.801-0.0100.201
NĂºmero de noches0.1160.000-0.0520.059-0.0300.5430.0250.0530.005-0.040-0.0421.0000.070-0.0300.077-0.0400.676-0.017
Pagado0.1250.0330.3830.2980.2770.0770.0810.4180.1210.3840.1880.0701.0000.2770.6020.5260.0350.294
Porcentaje_Ocupacion0.1180.0000.0470.1191.0000.0570.0000.1520.0190.0490.703-0.0300.2771.0000.0630.8980.0640.265
Portal de reserva0.0860.0580.5050.4750.0630.0510.0870.8020.1110.5041.0000.0770.6020.0631.0000.0980.0380.408
PosiciĂ³n0.1330.0340.0430.1940.8980.0610.0270.1840.0320.0510.801-0.0400.5260.8980.0981.0000.0670.295
Precio0.7780.0000.4150.0820.0640.8710.0620.0300.1950.474-0.0100.6760.0350.0640.0380.0671.0000.364
Precio_Medio_Historico0.4960.0440.4340.6830.2650.3180.0400.2950.1690.4820.201-0.0170.2940.2650.4080.2950.3641.000

Missing values

2024-08-28T08:55:00.040910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-28T08:55:00.311361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-28T08:55:00.482122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PosiciĂ³nLlegadaSalidaApartamentoPortal de reservaCreadoAdultosNiñosCheck-inCheck-outNotasPrecioDetalles de preciosComisiĂ³n incluidaPagadoAdelanto ya pagadoNĂºmero de nochesEstadoHuĂ©sped_TokenTelĂ©fono_TokenEmail_TokenNumero_ReservaMensaje_HuespedBOOKING_NOTEGENIUSNumero_HuespedesLead_TimeADRPrecio_Medio_HistoricoFechaApartamentos_OcupadosPorcentaje_Ocupacion
0324744702022-12-302023-01-01H BMA MONACOBooking.com2022-12-30 15:16:001.01.016:0011:00NĂºmero de reserva: 3509184945\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID **\n\nBOOKING NOTE : Payment charge is EUR 2.5839\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 234.90EUR234.9TVA - EUR 21.3535.23SĂ­No2Reservadobe87b7b4-734b-475a-aedc-b80420360db2e0c4f0b2-b56a-47ba-acb8-f6a35e572a768f5e9611-e2d7-4299-b5cd-8ffa3780cc213509184945Payment charge is EUR 2.5839True2.00 days 15:16:00117.45110.4279712022-12-3012.029.268293
1324707802022-12-302022-12-31H BMA HELSINKIBooking.com2022-12-30 13:38:002.00.016:0011:00NĂºmero de reserva: 3677697588\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID **\nApproximate time of arrival: between 14:00 and 15:00\nBOOKING NOTE : Payment charge is EUR 1.045\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 95.00EUR95.0TVA - EUR 8.6414.25SĂ­No1Reservado35e44167-2433-4876-b49b-a4545f1f7838415c6f96-47af-404d-aa92-720fc0218919513be7e2-5acb-40f1-994d-8b5b71755b3d3677697588Payment charge is EUR 1.045True2.00 days 13:38:0095.00111.2768752022-12-3012.029.268293
3324076182022-12-312023-01-01H BMA DUBLINBooking.com2022-12-28 20:36:004.00.016:0011:00NĂºmero de reserva: 2331981900\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID **\nApproximate time of arrival: between 17:00 and 18:00\nBOOKING NOTE : Payment charge is EUR 2.695\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 245.00EUR245.0TVA - EUR 22.2736.75SĂ­No1Reservado36ce4d92-6974-4f4d-be07-64ca05a4d8780a952103-56e5-4232-876f-eba3826afb2481c733f5-6f95-42db-a4ff-6d569ff29ee02331981900Payment charge is EUR 2.695True4.0-3 days +20:36:00245.00121.5113032022-12-3116.039.024390
4324015552022-12-282022-12-30HD-GARAJE 5Reserva directa2022-12-28 18:20:000.00.016:0011:00Trjeta 28.1240.0NaN0.00NoNo2Reservado5ea7bfa0-4a1a-471a-bbfd-c8ab3d45306badcea205-d9d3-45f5-84df-c636276779c2deb8b9cf-d436-405c-884e-890910c45722NaNNaNNaNFalse0.00 days 18:20:0020.0014.1545312022-12-2821.051.219512
5324003672022-12-312023-01-01HD DARIOBooking.com2022-12-28 17:53:002.00.016:0011:00NĂºmero de reserva: 3390950867\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID **\nBOOKING NOTE : Payment charge is EUR 2.211\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 201.00EUR201.0IVA - EUR 18.2730.15SĂ­No1Reservado9a4df539-fa60-48b4-ba02-c971b89cd539b240d5b6-2622-4361-a2d4-4815bb25f6472b196f6c-9987-441b-a20a-0ab7bdbcc1f23390950867Payment charge is EUR 2.211True2.0-3 days +17:53:00201.00108.6990082022-12-3116.039.024390
6323864532022-12-302022-12-31H BMA GARAJEReserva directa2022-12-28 12:14:000.00.016:0011:00Tarjeta el 2820.0NaN0.00NoNo1Reservadob4a1bb5d-b1ff-430d-aed2-0d7c14fe92872a189e60-4a59-4efe-9ee6-ba392ff23842f10d0bc9-6c2d-4ef8-afc9-ff8a74f2418cNaNNaNNaNFalse0.0-2 days +12:14:0020.0019.4550822022-12-3012.029.268293
7323806062022-12-282022-12-29H BMA AMSTERDAMBooking.com2022-12-28 09:31:003.00.016:0011:00NĂºmero de reserva: 3767273557\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID **\nBOOKING NOTE : Payment charge is EUR 2.453\nUse of parking lot is required. Please make a reservation.\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 223.00EUR223.0TVA - EUR 20.2733.45SĂ­No1Reservado24977c99-4a49-49f7-8b79-b242634b67a874366778-7b2f-40a3-a7c4-b88271cc04242d83ef75-5f19-4afc-b60f-b7e71200b0693767273557Payment charge is EUR 2.453True3.00 days 09:31:00223.00222.8639252022-12-2821.051.219512
8323717892022-12-292022-12-30H BMA BERLINBooking.com2022-12-27 22:22:002.02.016:0011:00NĂºmero de reserva: 3255965673\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID **\nBOOKING NOTE : Payment charge is EUR 2.046\nIl s'agit d'une rĂ©servation Smart Flex.\nConditions mises Ă  jour : annulation gratuite jusqu'Ă  1 jour avant l'arrivĂ©e.\nIl s'agit d'une rĂ©servation de remplacement. La rĂ©servation Smart Flex initiale n° 3478899786 est Ă  prĂ©sent remplacĂ©e dans son intĂ©gralitĂ©. Si le client paie Ă  l'arrivĂ©e, nous vous conseillons de valider la carte de crĂ©dit fournie dès que possible.\nSi vous souhaitez en savoir plus, consultez le lien suivant : https://admin.booking.com/hotel/hoteladmin/extranet_ng/manage/booking.html?hotel_id=5420998&res_id=3255965673\n\nPago por adelantado: 186.00EUR186.0TVA - EUR 16.9127.90SĂ­No1Reservado89bbed20-7c13-4d1a-bc20-14040d849e1f3b520c09-8acf-4018-aa96-fea5e1b20f77c8bd578f-c9cd-428a-b1c7-07c1c611b11d3255965673Payment charge is EUR 2.046False4.0-2 days +22:22:00186.00158.8981772022-12-2921.051.219512
9323692512022-12-292022-12-31H BMA CHICAGOBooking.com2022-12-27 21:12:002.02.016:0011:00NĂºmero de reserva: 2802243358\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID **\nBOOKING NOTE : Payment charge is EUR 4.345\nIl s'agit d'une rĂ©servation Smart Flex.\nConditions mises Ă  jour : annulation gratuite jusqu'Ă  1 jour avant l'arrivĂ©e.\nSi vous souhaitez en savoir plus, consultez le lien suivant : https://admin.booking.com/hotel/hoteladmin/extranet_ng/manage/booking.html?hotel_id=5420998&res_id=2802243358\nApproximate time of arrival: between 16:00 and 17:00\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 395.00EUR395.0TVA - EUR 35.9159.25SĂ­No2Reservado129da156-ca0c-43a6-b202-03321f40e6eae16f4fb4-c049-4a81-9665-7ea5d9122b5b9fda1188-7d57-4040-a992-9603cd98fea92802243358Payment charge is EUR 4.345True4.0-2 days +21:12:00197.50136.8109492022-12-2921.051.219512
10323333322022-12-292022-12-30H BMA MONACOBooking.com2022-12-26 22:17:002.02.016:0011:00NĂºmero de reserva: 2634389390\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID **\nIl s'agit d'une rĂ©servation Smart Flex.\nConditions mises Ă  jour : annulation gratuite jusqu'Ă  2 jours avant l'arrivĂ©e.\nSi vous souhaitez en savoir plus, consultez le lien suivant : https://admin.booking.com/hotel/hoteladmin/extranet_ng/manage/booking.html?hotel_id=5420998&res_id=2634389390\n\nBOOKING NOTE : Payment charge is EUR 1.287\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 117.00EUR117.0TVA - EUR 10.6417.55SĂ­No1Reservado7c210ac4-0181-415f-80a9-0d8b22f9a1ddd66c4f57-98b8-4371-8966-7a47906e8f8f1bf43087-449d-4455-b969-f73879373b472634389390Payment charge is EUR 1.287True4.0-3 days +22:17:00117.00110.3902002022-12-2921.051.219512
PosiciĂ³nLlegadaSalidaApartamentoPortal de reservaCreadoAdultosNiñosCheck-inCheck-outNotasPrecioDetalles de preciosComisiĂ³n incluidaPagadoAdelanto ya pagadoNĂºmero de nochesEstadoHuĂ©sped_TokenTelĂ©fono_TokenEmail_TokenNumero_ReservaMensaje_HuespedBOOKING_NOTEGENIUSNumero_HuespedesLead_TimeADRPrecio_Medio_HistoricoFechaApartamentos_OcupadosPorcentaje_Ocupacion
14376490030822023-12-262024-05-01HD BRUNOBooking.com2023-12-11 23:56:002.01.016:0011:00NĂºmero de reserva: 4002704862\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 13.98782\n\nDirecciĂ³n: Gh\nES\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 1271.62EUR1271.62IVA - EUR 115.6190.74SĂ­No10Reservadod672e667-58b6-49d6-834f-9eec2a8e5db53abf902a-39c8-4498-9537-a5bc4b6544e766738d3c-6df9-479b-8d0d-859474402db74002704862Payment charge is EUR 13.98782True3.0-15 days +23:56:00127.162102.4605352023-12-2631.075.609756
14377488002132024-03-022024-05-02H BMA AMSTERDAMBooking.com2023-08-11 16:16:006.02.016:0011:00NĂºmero de reserva: 4202562635\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 6.0335\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 548.50EUR548.50TVA - EUR 49.8682.28SĂ­No2Canceladod511d868-97ee-4fcb-81b9-0de9b76eaa2f3284ebf3-b0dc-4276-9630-bbb8a1ae40a3b9ac2aa8-dfd1-4328-8592-bed846c037104202562635Payment charge is EUR 6.0335True8.0-204 days +16:16:00274.250223.2126932024-03-0240.097.560976
14378486817942024-01-262024-01-28H BMA BERLINBooking.com2023-06-11 12:50:006.00.016:0011:00NĂºmero de reserva: 4128424493\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 5.104\n\nDirecciĂ³n: Arturo Alvarez Buylla n4 6B\nOviedo\nES\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 464.00EUR464.00TVA - EUR 42.1869.60SĂ­No2Reservadoc5ece72d-811b-4416-9020-1bc7189f04687a8b30f2-e77b-44c0-8d8c-948de75a5ddf87a911b1-0324-4a77-9f48-06d1aad302784128424493Payment charge is EUR 5.104True6.0-229 days +12:50:00232.000162.0164772024-01-2640.097.560976
14379482180902024-02-252024-02-26H BMA MONACOBooking.com2023-10-27 14:42:002.02.016:0011:00NĂºmero de reserva: 4254973846\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 0.891 Approximate time of arrival: between 11:00 and 12:00\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 81.00EUR81.00TVA - EUR 7.3612.15SĂ­No1Reservadobddb794d-b301-429b-9fe4-0506d161f77dd6175989-b148-44ab-beb3-94526eff5b94aa4e144a-0ccd-48a1-b174-cdbcfbf61d804254973846Approximate time of arrival: between 11:00 and 12:00Payment charge is EUR 0.891True4.0-121 days +14:42:0081.000118.9165062024-02-2540.097.560976
14380481790302024-01-222024-01-24HG1 ARTXANDAAirbnb2023-10-26 18:05:007.00.016:0011:00NĂºmero de reserva: HME2B4WMFN\nIdioma del huĂ©sped: es327.80Cancellation Host Fee - EUR 44.7\nCancellation Payout - EUR 283.144.70NoNo2Canceladof865e062-dc87-4fc6-8f71-44891bfb23cf1c48cd47-5790-4cfc-a351-428c55590dccb9423ed4-d4ac-47da-b723-70a57178a91eNaNNaNNaNFalse7.0-88 days +18:05:00163.900188.3582052024-01-2240.097.560976
14381480203652024-06-172024-06-21H - BUA 3PReserva directa2023-10-23 12:17:001.00.016:0011:00Viene con un grupo del hostel.520.00NaN0.00NoNo4Reservado3f799401-19e1-42e6-bef4-fb4ea4ea89eaaa1f4b17-0bc4-4b9b-a997-34607ef59b96dc47a9ec-df04-470b-bad7-f77be13f4f44NaNNaNNaNFalse1.0-238 days +12:17:00130.00084.0245582024-06-1740.097.560976
14382466706292024-11-022024-12-02H BMA AMSTERDAMBooking.com2023-09-26 14:51:007.00.016:0011:00NĂºmero de reserva: 4232587055\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** BOOKING NOTE : Payment charge is EUR 1.75175\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 159.25EUR159.25TVA - EUR 14.4823.89SĂ­No1Cancelado9e67d79d-6df1-4eb0-bb8f-ab6c4d6b8f358d2598f6-09a7-45b6-b6df-56f31168bd6a80331c7b-6d26-4173-83ab-bd0ef9cab8f24232587055Payment charge is EUR 1.75175True7.0-403 days +14:51:00159.250240.8247672024-11-0240.097.560976
14383466423212024-12-012024-01-14H BMA BERLINBooking.com2023-09-25 23:26:006.00.016:0011:00NĂºmero de reserva: 4237415932\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** Approximate time of arrival: between 17:00 and 18:00 BOOKING NOTE : Payment charge is EUR 13.1274\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 1193.40EUR521.60TVA - EUR 47.4278.24SĂ­No2Canceladoeb94c289-3eee-433c-9966-b16fdb114ddd707e7188-43ba-4a80-b705-5efc1e895e660b46585e-a500-4cdc-9f54-e4017a1d8ec54237415932Payment charge is EUR 13.1274True6.0-433 days +23:26:00260.800163.8066292024-12-0138.092.682927
14384466423182024-12-012024-01-14H BMA AMSTERDAMBooking.com2023-09-25 23:26:005.00.016:0011:00NĂºmero de reserva: 4237415932\nMensaje del huĂ©sped: ** THIS RESERVATION HAS BEEN PRE-PAID ** Approximate time of arrival: between 17:00 and 18:00 BOOKING NOTE : Payment charge is EUR 13.1274\n\nMĂ¡s informaciĂ³n: booker_is_genius\nPago por adelantado: 1193.40EUR671.80TVA - EUR 61.07100.77SĂ­No2Cancelado5e5b0b39-996b-4856-b0c6-4ce853e664af3c361138-e3ee-408e-bbfc-5e093f84fd9b3bfa3195-68fa-4d88-bd73-5c14c41e88ef4237415932Payment charge is EUR 13.1274True5.0-433 days +23:26:00335.900241.2183142024-12-0138.092.682927
14385450055522024-08-012024-10-01HG0 MUGARRAAirbnb2023-08-26 11:09:007.00.016:0011:00NĂºmero de reserva: HM9ENYC99S\nIdioma del huĂ©sped: en261.80Cancellation Host Fee - EUR 35.7\nCancellation Payout - EUR 226.135.70SĂ­No2Reservadof7796185-6cbb-4635-8ae2-acaf7d8c7ce48af45e0d-0e83-4ac4-b980-923c5568d423bd13918b-0703-474c-bf94-63efc036e530NaNNaNNaNFalse7.0-341 days +11:09:00130.900182.3860702024-08-0140.097.560976